Reliability and validity in comparative studies of software prediction models

Empirical studies on software prediction models do not converge with respect to the question "which prediction model is best?" The reason for this lack of convergence is poorly understood. In this simulation study, we have examined a frequently used research procedure comprising three main ingredients: a single data sample, an accuracy indicator, and cross validation. Typically, these empirical studies compare a machine learning model with a regression model. In our study, we use simulation and compare a machine learning and a regression model. The results suggest that it is the research procedure itself that is unreliable. This lack of reliability may strongly contribute to the lack of convergence. Our findings thus cast some doubt on the conclusions of any study of competing software prediction models that used this research procedure as a basis of model comparison. Thus, we need to develop more reliable research procedures before we can have confidence in the conclusions of comparative studies of software prediction models

An Empirical analysis of Open Source Software Defects data through Software Reliability Growth Models

The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects. For each release of the selected projects two types of datasets have been created; datasets developed with respect to defect creation date (created date DS) and datasets developed with respect to defect updated date (updated date DS). These defects datasets are modelled by eight SRGMs; Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model. These models are chosen due to their widespread use in the literature. The SRGMs are fitted to both types of defects datasets of each project and the their fitting and prediction capabilities are analysed in order to study the OSS reliability growth with respect to defects creation and defects updating time because defect analysis can be used as a constructive reliability predictor. Results show that SRGMs fitting capabilities and prediction qualities directly increase when defects creation date is used for developing OSS defect datasets to characterize the reliability growth of OSS. Hence OSS reliability growth can be characterized with SRGM in a better way if the defect creation date is taken instead of defects updating (fixing) date while developing OSS defects datasets in their reliability modellin

The Multidimensional Perfectionism Cognitions Inventory–English (MPCI-E): Reliability, validity, and relationships with positive and negative affect

The Multidimensional Perfectionism Cognitions Inventory (MPCI; Kobori & Tanno, 2004) is a promising new instrument developed in Japan to assess perfectionism cognitions regarding personal standards, pursuit of perfection, and concern over mistakes. The present study examined reliability and validity of the English version of the MPCI, the MPCI-E (Kobori, 2006), in a sample of 371 native English speakers. A confirmatory factor analysis confirmed the MPCI-E’s three-factorial oblique structure. Moreover, correlations with measures of dispositional perfectionism and past-week positive and negative affect provided first evidence of the MPCI-E’s convergent and differential validity. Finally, hierarchical multiple regressions indicated that the MPCI-E showed incremental validity in explaining variance in positive and negative affect above variance explained by dispositional perfectionism. Overall, the findings provide first evidence for the reliability and validity of the MPCI-E as a multidimensional measure of perfectionism cognitions that has the potential to further our understanding of positive and negative cognitions in perfectionism

The determination of measures of software reliability

Measurement of software reliability was carried out during the development of data base software for a multi-sensor tracking system. The failure ratio and failure rate were found to be consistent measures. Trend lines could be established from these measurements that provide good visualization of the progress on the job as a whole as well as on individual modules. Over one-half of the observed failures were due to factors associated with the individual run submission rather than with the code proper. Possible application of these findings for line management, project managers, functional management, and regulatory agencies is discussed. Steps for simplifying the measurement process and for use of these data in predicting operational software reliability are outlined

Defect prediction with bad smells in code

Background: Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One of the key elements to gain effective software defect prediction is proper selection of metrics used for dataset preparation. Objective: The purpose of this research is to verify, whether code smells metrics, collected using Microsoft CodeAnalysis tool, added to basic metric set, can improve defect prediction in industrial software development project. Results: We verified, if dataset extension by the code smells sourced metrics, change the effectiveness of the defect prediction by comparing prediction results for datasets with and without code smells-oriented metrics. In a result, we observed only small improvement of effectiveness of defect prediction when dataset extended with bad smells metrics was used: average accuracy value increased by 0.0091 and stayed within the margin of error. However, when only use of code smells based metrics were used for prediction (without basic set of metrics), such process resulted with surprisingly high accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data anomalies and problems we observed when two different metric sources were used to prepare one, consistent set of data. Conclusion: Extending the dataset by the code smells sourced metric does not significantly improve the prediction effectiveness. Achieved result did not compensate effort needed to collect additional metrics. However, we observed that defect prediction based on the code smells only is still highly effective and can be used especially where other metrics hardly be used.Comment: Chapter 10 in Software Engineering: Improving Practice through Research (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 163-176, 201